Studying at York
Digital Signal Processing - ELE00079H
- Department: Electronic Engineering
- Credit value: 20 credits
- Credit level: H
- Academic year of delivery: 2024-25
- See module specification for other years: 2023-24
Module summary
This module builds on second year Maths, Signal & Systems to introduce discrete time signal processing techniques suitable for software implementation. We will introduce discrete time techniques routinely used in Digital Signal Processing (DSP) systems, including the discrete time Fourier transform (DTFT), discrete Fourier transform (DFT) and discrete time convolution and correlation. The importance of data windows in DSP will be highlighted and a range of data windows will be introduced, including the raised cosine family (Hanning, Hamming, Blackmann) and orthogonal multi-taper (DPSS) windows. Frequency analysis of signals will be described including practical aspects of spectral leakage, analysis of stochastic signals and time-frequency analysis using spectrograms. Practical applications of these techniques will be considered using a range of different data modalities including biomedical, environmental and speech data. The difference equation as a key design tool in DSP will be introduced and its use in describing digital filters will be presented. The window method for Finite Impulse Response (FIR) filter design will be described, covering both theoretical and practical aspects. Machine learning in DSP systems will be introduced and the theory and application of deep Convolutional Neural Networks (CNN) presented, with a focus on image recognition including standard benchmark applications (MNIST, ImageNet). Practical applications will be covered throughout the course with a focus on algorithm development and use of toolboxes in MATLAB. A number of practical sessions are included to develop practical skills in DSP using MATLAB, which will include analysis of the different data sets presented in the lectures
Professional requirements
Related modules
Pre-requisite modules
Co-requisite modules
- None
Prohibited combinations
- None
Additional information
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Semester 2 2024-25 |
Module aims
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To introduce and develop an understanding of the discrete Fourier transform.
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To consider practical aspects in the design and application of FFT algorithms.
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To introduce and develop an understanding of discrete convolution and discrete correlation.
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To describe the use of data windows in DSP.
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To introduce the difference equation approach for the study of discrete time systems.
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To consider practical examples of time and frequency analyses of discrete signals.
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To develop the sampling theorem for sampling and reconstructing an analogue signal.
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To present the theory and application of the window method for FIR filter design.
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To introduce machine learning techniques for digital signal processing by studying deep convolutional neural networks for image and audio processing.
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To develop practical skills in applying DSP and machine learning techniques in MATLAB
Module learning outcomes
Subject content learning outcomes
After successful completion of this module, students will be able to:
- Analyse the frequency content of discrete time signals using discrete Fourier transform analyses.
- Assess the consequences of different data windows on signal analysis and filter performance.
- Discuss the differences between discrete convolution and discrete correlation and the different scenarios where these can be applied.
- Create software implementations for FIR filters from a formal specification.
- Discuss the principles of supervised learning in neural networks as the basis for machine learning in DSP systems.
Graduate skills learning outcomes
After successful completion of this module, students will be able to:
- State technical concepts in DSP concisely and accurately in written documents
- Implement and test algorithms in MATLAB, analyse discrete signals, make inference and present results.
- Plan and manage time in a laboratory setting
Module content
Indicative assessment
| Task | % of module mark |
|---|---|
| Closed/in-person Exam (Centrally scheduled) | 40 |
| Essay/coursework | 60 |
Special assessment rules
None
Additional assessment information
Coursework assessment, worth 60%. The coursework should be in the form of a written technical report that conforms to IEEE standards. The length of the report is capped at 4 pages and should be written using the publicly available IEEE template (Word or LaTex). No word count is specified, but your assessment must fit within the 4 page limit.
Indicative reassessment
| Task | % of module mark |
|---|---|
| Closed/in-person Exam (Centrally scheduled) | 40 |
| Essay/coursework | 60 |
Module feedback
'Feedback’ at a university level can be understood as any part of the learning process which is designed to guide your progress through your degree programme. We aim to help you reflect on your own learning and help you feel more clear about your progress through clarifying what is expected of you in both formative and summative assessments. A comprehensive guide to feedback and to forms of feedback is available in the Guide to Assessment Standards, Marking and Feedback.
The School of PET aims to provide some form of feedback on all formative and summative assessments that are carried out during the degree programme. In general, feedback on any written work/assignments undertaken will be sufficient so as to indicate the nature of the changes needed in order to improve the work. The School will endeavour to return all exam feedback within the timescale set out in the University's Policy on Assessment Feedback Turnaround Time. The School would normally expect to adhere to the times given, however, it is possible that exceptional circumstances may delay feedback. The School will endeavour to keep such delays to a minimum. Please note that any marks released are subject to ratification by the Board of Examiners and Senate. Meetings at the start/end of each term provide you with an opportunity to discuss and reflect with your supervisor on your overall performance to date.
Statement of Feedback
Formative Feedback
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Problem sheets will be provided and marked in tutorial workshops, and you will have the opportunity to discuss your progress with the course tutor.
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Regular lab sessions will provide the opportunity to ask questions and receive verbal help and feedback about your progress in developing practical skills.
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Questions can be asked at any time during the in-class sessions or be Email, and will be answered as soon as possible.
Summative Feedback
Individual feedback will be provided on your written assessment.
Indicative reading
"Digital Signal Processing: Concepts and Applications" by Bernard Mulgrew, Peter Grant and John Thompson Palgrave Macmillan, 2nd Edition, ISBN 0-333-96356-3.